Effective anti-fraud solutions don’t just look for fraud that is underway. Instead, they look for signs that fraud is likely to happen. They detect fraud before it occurs, helping to reduce losses, save time, and safeguard your reputation.
To prevent fraud, these solutions rely on a hybrid approach that uses multiple types of data analytics, and as you search for a fraud solution, you’re likely to bump into a range of different techniques. To help you choose the best solution for your organization’s anti-fraud strategy, this guide explains the basics of six common types of data analytics.
1. Automated Business Rules
Business rules are conditional statements that create the foundation of an automated analysis system. Automated business rules allow software engineers to translate an organization’s requirements into automated workflows. Here’s a simple example: when a financial institution says that all deposits over a certain dollar amount should be manually reviewed by a manager.
However, rule sets can also be much more complicated. They can look at multiple data points and infer assumptions if certain conditions have been met. Here’s an example of a more complicated business rule in action — flagging a transaction as potentially fraudulent because it originates from a new account and the new account holder has just changed the address on the account.
2. Predictive Modeling
Predictive modeling is a type of data mining that looks for patterns and anomalies in large data sets to predict future trends. Here’s an example: imagine an artificial intelligence looks at data related to thousands of transactions from a financial institution and is told that the majority of the transactions are legitimate.
By analyzing all of the data points related to the transactions, the AI can recognize patterns and anomalies. It can figure out which details indicate that a transaction is legitimate, and which data points signify fraud.
Then, the AI can use this knowledge when it assesses future transactions for signs of fraud. In particular, it can learn patterns that suggest a heightened risk of fraud. For instance, the AI may learn that if certain actions happen in a certain sequence, fraud is likely to happen, and on the flip side, those actions in a different sequence or context may predict that a customer is completing a legitimate transaction.
3. Text Mining
Also called natural language processing, text mining is a type of data mining that looks at text to spot potential signs of fraud, and it’s particularly useful at evaluating multiple, diverse elements of a transaction.
To give you an example, imagine that someone purchases something online. There are no details in the purchase that raise any red flags, but multiple details surrounding the transaction indicate a potential risk of fraud.
These details include a fake email address from an empty domain name, unusual timing of the transaction, purchasing an item that has a high correlation with fraud, an IP address located in one country but owned by someone from another country, and a phone number linked to fraud based on an internet search. These types of details can typically only be discovered by a solution that incorporates text mining.
4. Database Searches
There are multiple databases that hold information about fraud cases, and a fraud detection and prevention solution that includes databases searches looks through these databases to find signs of fraud. For instance, if someone tries to open a new account at your financial institution and their phone number is in a database and linked to other fraud incidents, you will be alerted.
5. Exception Reporting
Exception reporting is when an anti-fraud solution identifies activities that vary from the standard, normal, expected processes. It highlights the exceptions and makes your team aware of them.
Exception reporting can help you identify individual instances of fraud, but it can also help you see where fraud is getting past your controls and help you determine where to direct your resources or how to establish your priorities.
6. Network Link Analysis
Link analysis looks for relationships between nodes of data, and it allows anti-fraud solutions to look for fraud in an interactive and intuitive manner. For instance, link analysis may scour multiple new account applications and realize that the same physical address is paired with multiple Social Security Numbers. It may discover that a new applicant has a limited credit history, no known relatives, and a brand new phone number, and together, those elements present a strong case for fraud.
Ultimately, these analytics work together to detect potential fraud as efficiently and accurately as possible. Along with artificial intelligence (AI) and machine learning, these analytic approaches assess multiple data points related to every transaction including non-monetary transactions, and ultimately, they detect fraud more effectively while reducing the risk of false positives.
Contact SQN Banking Systems Today
To learn more about these concepts and other elements used in fraud prevention, contact us today. At SQN Banking Systems, we use sophisticated algorithms and real-time detection to help our clients reduce their risk of fraud. We focus on fraud, so our clients can focus on the other aspects of running a successful financial institution.